Reversecontact Python API Docs | dltHub

Reversecontact is a REST enrichment API that returns person and company profile data from an email or domain. The REST API base URL is https://api.reversecontact.com and all requests require an API key passed as a query parameter.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Reversecontact data in under 10 minutes.

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What data can I load from Reversecontact?

Here are some of the endpoints you can load from Reversecontact:

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How do I authenticate with the Reversecontact API?

Reversecontact uses an API key named "apikey" that must be included in every request as a query parameter, e.g., ?apikey=YOUR_KEY.

1. Get your credentials

1. Sign up at https://app.reversecontact.com/register.
2. After confirming your email, log in to the dashboard.
3. Click "Integrations" in the sidebar.
4. Copy the API Key displayed and use it as the "apikey" query parameter in requests.

2. Add them to .dlt/secrets.toml

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[sources.reversecontact_source]
apikey = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Reversecontact API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python reversecontact_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline reversecontact_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset reversecontact_data
The duckdb destination used duckdb:/reversecontact.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline reversecontact_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads person_enrichment and person_profile from the Reversecontact API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def reversecontact_source(apikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.reversecontact.com",
            "auth": {
                "type": "api_key",
                "apikey": apikey,
            },
        },
        "resources": [
            {"name": "person_enrichment", "endpoint": {"path": "enrichment"}},
            {"name": "person_profile", "endpoint": {"path": "enrichment/profile"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="reversecontact_pipeline",
        destination="duckdb",
        dataset_name="reversecontact_data",
    )
    load_info = pipeline.run(reversecontact_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("reversecontact_pipeline").dataset()
sessions_df = data.person_enrichment.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM reversecontact_data.person_enrichment LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("reversecontact_pipeline").dataset()
data.person_enrichment.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Reversecontact data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If the apikey parameter is missing or invalid the API returns a 401 status with a message such as "Unauthorized" or "The API Key is missing".

Insufficient credits / payment required

When the account lacks sufficient credits the API responds with 402 and a message like "You have to upgrade to continue" or "You don't have enough credits on your account".

Rate limiting / Too Many Requests

Exceeding the daily allowance returns a 429 status. Successful responses include a rate_limit_left field indicating remaining quota.

Common request errors

• 400 Bad Request – missing or malformed parameters (e.g., missing email).
• 404 Not Found – no matching profile was found.
• 500 Server Error – internal server error; response contains success: false and an explanatory msg field.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack